To realize our full potential, RTX is committed to creating a company where all employees are respected, valued and supported in the pursuit of their goals. We know companies that embrace diversity in all its forms not only deliver stronger business results, but also become a force for good, fueling stronger business performance and greater opportunity for employees, partners, investors and communities to succeed.

The following position is to join our RTX Technology Research Center (RTRC) Team:

RTRC serves as the innovation hub for RTX. We conduct basic and applied research in a stimulating multi-disciplinary environment where scientists, engineers, practitioners, and subject matter experts collaborate and exchange experience. We transform that research into the solutions and products that help our businesses shape the future. RTRC is a multidisciplinary group collaborating on groundbreaking innovations. Our team is working to anticipate the discoveries destined to change everything, and they transform that research into the solutions that help our businesses shape the future of aerospace and defense.

The Dynamics, Controls, and Autonomy team at RTRC supports dynamical system analysis and modeling, control system analysis and design, and autonomous systems research for all RTX business units, including both development of novel solutions for future products and solving the toughest problems with current products. In parallel, we are working with government customers on more broadly applicable technology.

We are looking for a highly motivated summer intern eager to apply cutting-edge techniques in collaborative autonomy or advanced control, enhanced by machine learning methods.

What You Will Do

As a summer intern, you will have the opportunity to contribute to research projects in one of the following areas:

• Collaborative Autonomy: Assist with projects involving multi-agent autonomous systems, developing behaviors for collaborative teams of unmanned vehicles, implement behaviors for mobile aerial and ground robots, validate and demonstrate autonomous behaviors in simulation and hardware experiment

• Embedded Real-time control: Assist with projects involving aircraft anti-lock braking system (ABS) modeling, simulation and control development, Turbomachinery rotor speed regulation to emulate Foreign Object strike detection development and validation

• Machine Learning-based Control: Assist with projects involving design and implementation of advanced control solutions for applications like jet engines, electric propulsion systems, missiles, autonomous vehicles, or space systems. Integrate machine learning with control systems, prioritizing stability and safety.

Qualifications You Must Have

• Currently pursuing a graduate degree (Master's or Ph.D.) in Mechanical, Aerospace, Electrical, or a related Engineering discipline. Candidates must not graduate prior August 2025. Please submit a copy of your academic transcripts with your application.
• U.S. Person (U.S. citizen, permanent resident, refugee or asylee) or eligible to obtain necessary export authorizations required. https://www.ecfr.gov/current/title-22/chapter-I/subchapter-M/part-120/subpart-C/section-120.62

Qualifications We Prefer

• Experience in one or more of the following areas of Collaborative Autonomy: multi-agent mission planning, trajectory generation, perception algorithms for navigation, modeling and simulation of autonomous systems, or communications. Experience with programming languages (e.g., Python, C, C++, CUDA) and ROS.
• Experience in one or more of the following areas of Real-time Embedded Control: motor control technologies, dynamics, and mechanical systems modeling. Programming languages like Matlab, Python and C/C++. Experience with dSpace or Speedgoat or FPGA programming. Demonstrated hands-on development.
• Experience in one or more of the following areas of Machine Learning-based Control: model-based control (e.g., Model Predictive Control), multivariable control, estimation, and control-oriented physical modeling. Machine learning and deep learning fundamentals, reinforcement learning, and physics-informed neural networks (PINNs). MATLAB/Simulink for modeling and control design. Python programming with experience in TensorFlow and PyTorch.

• Strong analytical, problem-solving, and communication skills, with a willingness to work in a collaborative team environment.
• Adaptability and the ability to learn quickly, with a proactive attitude and eagerness to take on new challenges.
• A passion for applying theoretical concepts to real-world problems and the ability to think innovatively when approaching complex problems.

Learn More & Apply Now!
Location: This position is in East Hartford, CT and is a hybrid role.
Please consider the following role type definition as you apply for this role:
Hybrid: Employees who are working in Hybrid roles will work regularly both onsite and offsite. Ratio of time working onsite will be determined in partnership with your leader.

The salary range for this role is 37,000 USD - 82,000 USD. The salary range provided is a good faith estimate representative of all experience levels. RTX considers several factors when extending an offer, including but not limited to, the role, function and associated responsibilities, a candidate's work experience, location, education/training, and key skills.

Hired applicants may be eligible for benefits, including but not limited to, medical, dental, vision, life insurance, short-term disability, long-term disability, 401(k) match, flexible spending accounts, flexible work schedules, employee assistance program, Employee Scholar Program, parental leave, paid time off, and holidays. Specific benefits are dependent upon the specific business unit as well as whether or not the position is covered by a collective-bargaining agreement.

Hired applicants may be eligible for annual short-term and/or long-term incentive compensation programs depending on the level of the position and whether or not it is covered by a collective-bargaining agreement. Payments under these annual programs are not guaranteed and are dependent upon a variety of factors including, but not limited to, individual performance, business unit performance, and/or the company's performance.

This role is a U.S.-based role. If the successful candidate resides in a U.S. territory, the appropriate pay structure and benefits will apply.

RTX anticipates the application window closing approximately 40 days from the date the notice was posted. However, factors such as candidate flow and business necessity may require RTX to shorten or extend the application window.

RTX is An Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or veteran status, age or any other federally protected class.

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